Integrated actuators – Prof. Y. Perriard
Note: Projects are intended for Microengineering, Electrical Engineering, Computer Science and Mechanical Engineering sections.
For information and registrations contact:
- Prof. Yves Perriard at: yves.perriard@epfl.ch
- Paolo Germano at: paolo.germano@epfl.ch
Transportation fees between EPFL and Neuchâtel will be covered.
- Project # 1 – Advanced control of dielectric elastomer actuators
- Simon Holzer
Dielectric Elastomer Actuators (DEAs) are deformable materials frequently employed in soft robotics, distinguished by high energy density. Often referred to artificial muscles, they deform based on the electrostatic principle, where the application of high voltage induces thickness reduction which will lead to an increase of surface in the other directions.
This project aims to apply an advanced control algorithm for soft robots to enable small and fast displacements. DEAs are non-linear systems and traditional control strategies like PIDs lack in precision. Thus, the task of this semester project would be to integrate an improved control algorithm (e. g. model predictive control). During the project, the student will do a study on the state of the art, implement the algorithm in a chosen environment (LabView/Python or others possible) and test the functionality by experiments on soft robots. Successful completion of ME-425 Model Predictive Control course is beneficial and recommended.
The following tasks will be done during your project:
– Performing a state of the art about existing control algorithms for soft robots and further needed information for the project
– Choice and implementation of a promising control algorithm
– Experiments on existing DEA platform to show abilities of the implemented method
– Writing project report
Keywords: Model predictive control, soft robotics, biomedical engineering
S. Holzer, B. Tiwari, S. Konstantinidi, Y. Civet, Y. Perriard, in: ACTUATOR 2024; International Conference and Exhibition on New Actuator Systems and Applications, Wiesbaden (2024) VDE Verlag
. - Project # 2 – Dielectric elastomer actuators for new applications in biology
- Simon Holzer
The Center for Artificial Muscles (CAM) in Neuchâtel is specialised in the development of soft robotics devices for medicine and biology [1]. Our soft robots are based on dielectric elastomer actuators (DEA) which converts electrical energy into mechanical energy.
The use of dielectric elastomer actuators in different research fields promises new applications. By applying a mechanical stimulation, human cells are stimulated which leads to changes, for example, in the cell cycle. Besides the mechanical stimulation, DEAs can offer, if used in a correct manner, other advantages useful in biology.
The aim of this project is to research the ability of DEAs as actuators in the field of acoustofluidics. The student will work on an interdisciplinary project which connects insights from fluid dynamics, soft robotics and biology.
The following tasks will be done during your project:
– Performing a state of the art about existing actuators in cell biology, acoustofluidics and further needed information for the project
– Proof of concept
– Implementation of physical model
– Experimental testing in a real situation containing DEAs
– Writing project report
Keywords: Acoustofluidics, soft robotics, biomedical engineering
D. V. Deshmukh, P. Reichert, J. Zvick, C. Labouesse, V. Künzli, O. Dudaryeva, O. Bar-Nur, M. W. Tibbitt, J. Dual, Advanced functional materials (2022) 32,30.
. - Project # 3 – Simulation and implementation of a wireless power module for dielectric elastomer actuator
- Maribel Caceres Rivera, Paolo Germano
Description:
In many applications for implantable devices requires a battery to operate. Such batteries present various limitation for long term transcutaneous devices. Especially for people which required operation to replace the battery and the operation causes a high risk. More and more application for implantable devices uses wireless power technologies, what present a reliable solution in health application as ventricular assistance devices, neuroimplants, eye power lens, and etc. In this project the goal is to develop a wireless Implantable module that allows to transfer energy from a transmitter on the outside to the receiver inside the skin; to provide energy to a Dielectric Elastomer actuator.
The design of the wireless power module consider the next challenges:
– Modeling of the transmitter and receiver: Total amount of power in TX and RX
– Modeling the behavior of the transmission considering the tissue as medium
– Consider Biosafety: SAR (Specific absorption rate), heating tissue and magnetic field
– Alignment between TX and RX
– Efficiency and Losses
– Dimension
Skills:
COMSOL, PCB design (Altium, Kicad, OrCAD…)
Simulation tools (LTSPICE, PSPICE or SPICE KEYSIGHT )
Papers related:
[1] K. N. Bocan, M. H. Mickle and E. Sejdić, “Multi-Disciplinary Challenges in Tissue Modeling for Wireless Electromagnetic Powering: A Review,”
[2] Khan, S.R.; Pavuluri, S.K.; Cummins, G.; Desmulliez, M.P.Y. Wireless Power Transfer Techniques for Implantable Medical Devices: A Review
. - Project # 4 – Multiple motors controller with sensorless position control
- Maël Dagon
Context
Diabetic patients often suffer from peripheral neuropathy, which causes foot insensitivity leading to high plantar pressure points (HPP). HPP can lead to ulcers and potentially amputations. To redistribute plantar pressure, we developed a reconfigurable insole containing an array of offloading modules (Figure 1).
Objective
Each module is driven by a small DC motor and can be moved up or down with a range of ~5mm. The project consists in the development of a motor controller which can drive numerous modules (motors) simultaneously. The driver must be modulable to accept more motors with little hardware modification. The driver must also use the motor characteristics, and its voltage and current feedback to perform sensorless positioning of the modules.
Content of the project
Control board development: Choose an architecture to work with (Arduino, ESP32, STM32, …) and develop the control electronics for the motors.
Software development: Develop the sensorless strategy for positioning of the modules. Implement the control strategy with the microcontroller and control board.
Board validation: Build a PCB for the controller, validate good functioning of the hardware and assess performance of the positioning strategy.
Profile
Type of work: 20% theory / 30% hardware / 30% software / 20% experiments
– Good knowledge in control electronics, data acquisition and programming
– Knowledge in motor physics
– Ability to work autonomously
– Ability to setup and conduct relevant experimental tests
Figure 1 Array of offloading modules
. - Project # 5 – Multi-axial sensing insole for intelligent offloading footwear
Andres Osorio Salazar
Every 20 seconds, someone with diabetes is subjected to amputation somewhere in the world. The primary risk factor for these amputations is the development of ulcerations in the skin, after abnormally high plantar pressures (PP) and/or shear stress [1], coupled with neuropathy, the lack of sensation in the feet. The plantar pressure distribution changes throughout the day according to various uncontrollable factors [2], such as activity, walking surface type, posture adjustments, etc. Therefore, an intelligent offloading system that can autonomously and continuously redistribute local PPs is highly desirable and could potentially reduce the incidence of amputations in people with diabetes.
In our laboratory, we have successfully developed an intelligent offloading shoe, which operates using magnetorheological fluid (MRF) modules for actuation [3]. We use an in-house developed piezoresistive sensing insole to provide PP feedback to the controller, allowing the system to determine the appropriate offloading locations in real time [4].
With the aim of including both normal and shear PP sensing capabilities, which would provide more in-depth information of the stress on the skin of the foot, thus potentially aiding in the offloading decision algorithm, we propose a project for the development of a new multi-axis PP sensing insole. The maximum sensing range of this sensor should be > 200 kPa and 700 kPa for shear and normal PP respectively, corresponding to a sensing area < 100 mm2, so that it can be integrated with our offloading system.
The following tasks will be done during the project:
– Evaluation of state of the art of multiaxial pressure sensors and their manufacture
– Selection of manufacturing and design of novel sensing insole
– Manufacture, validation and characterization of a sensing insole prototype
– Design, manufacture and testing of data acquisition electronics
– Writing of a scientific project report and presentation
The following skills would be desirable for an applicant:
– Basic knowledge of electronics and circuit prototyping
– Ability to troubleshoot, analyze data, and optimize designs for efficiency and reliability
– Experience collaborating on group projects and clearly explaining technical concepts
– Willingness to learn new tools, technologies, and concepts relevant to the project
With your participation in this project, you would not only advance the technology available for patients suffering from diabetes, but you would also benefit from the experience and knowledge of our team, and will gain skills in:
– Advanced sensor technology and materials science
– The design and integration of smart systems for medical applications
– Real-time data acquisition and processing techniques
– Development and testing of innovative healthcare solutions
References:
1. Lekkala, J. (2014). Plantar shear stress measurements—A review. Clinical Biomechanics, 29(5), 475-483.
2. Liau, B., Wu, F. L., Li, Y., Lung, C., Mohamed, A. A., & Jan, Y. (2021). Effect of walking speeds on complexity of plantar pressure patterns. Complexity, 2021(1).
3. Hemler, S. L., Ntella, S. L., Jeanmonod, K., Köchli, C., Tiwari, B., Civet, Y., … & Pataky, Z. (2023). Intelligent plantar pressure offloading for the prevention of diabetic foot ulcers and amputations. Frontiers in Endocrinology, 14, 1166513.
4. Tiwari, B., Ntella, S. L., Jeanmonod, K., Germano, P., Koechli, C., Pataky, Z., … & Perriard, Y. (2023). A Polyester-Nylon Blend Plantar Pressure Sensing Insole for Person with Diabetes. IEEE Sensors Letters.
.- Project # 6 – Sedimentation & clogging management device for magnetorheological valves in intelligent offloading footwear
- Andres Osorio Salazar
Every 20 seconds, someone with diabetes is subjected to amputation somewhere in the world. The primary risk factor for these amputations is the development of ulcerations in the skin, after abnormally high plantar pressures (PP) and/or shear stress [1], coupled with neuropathy, the lack of sensation in the feet. The plantar pressure distribution changes throughout the day according to various uncontrollable factors [2], such as activity, walking surface type, posture adjustments, etc. Therefore, an intelligent offloading system that can autonomously and continuously redistribute local PPs is highly desirable and could potentially reduce the incidence of amputations in people with diabetes.
In our laboratory, we have successfully developed an intelligent offloading shoe, which uses an in-house developed piezoresistive sensing insole to provide PP feedback to the controller, allowing the system to determine the appropriate offloading locations in real time [2]. The detected high-pressure areas are then offloaded using miniature magnetorheological fluid (MRF) modules that can change their stiffness according to an electromagnetic field [3].
The MRF offloading modules consist of two reservoirs connected by a flow channel equipped with a coil to regulate the surrounding magnetic field. In the absence of a magnetic field, the MRF, a suspension of magnetic particles in oil, flows freely from the top reservoir to the bottom under axial compression, which deforms the bellows. However, when a magnetic field is applied, the MRF’s viscosity increases significantly, preventing it from passing through the channel and effectively blocking any displacement caused by compression.
A significant challenge for our offloading shoe, as well as for magnetorheological devices in general, is sedimentation. Over time, the suspended magnetic particles settle inside the valves when the device is not in use, preventing the proper functioning of the offloading modules. Additionally, repeated activation cycles can magnetize the particles, leading to the formation of clumps that obstruct the flow channels and further impair module operation.
To address these challenges, we propose a student project to develop a device that mitigates both issues. The device will keep the offloading shoes in motion while not in use and incorporate a degaussing coil to generate an alternating magnetic field that gradually diminishes in strength. This device will help prevent sedimentation and demagnetize the system, ensuring the continuous and reliable operation of the intelligent offloading footwear.
The following tasks will be done during the project:
– Evaluation of state of the art of sedimentation and clogging management systems for magnetorheological devices
– Motion mechanism, demagnetization apparatus design
– Mechanical, electrical and integration of the device
– Control and programming of the device, including user interface is possible
– Testing and validation
– Writing of a scientific project report and presentation
The following skills would be desirable for an applicant:
– Basic knowledge of mechanical systems, electronics, and programming (e.g., microcontrollers, circuit design, and motion mechanisms)
– Ability to troubleshoot, analyze data, and optimize designs for efficiency and reliability
– Experience collaborating on group projects and clearly explaining technical concepts
– Willingness to learn new tools, technologies, and concepts relevant to the project
With your participation in this project, you would not only advance the technology available for patients suffering from diabetes, but you would also benefit from the experience and knowledge of our team, and will gain skills in:
– Mechanical and electronic design and prototyping
– Embedded programming and interface design
– Analytical and Problem-Solving Skills
References:
1. Lekkala, J. (2014). Plantar shear stress measurements—A review. Clinical Biomechanics, 29(5), 475-483.
2. Tiwari, B., Ntella, S. L., Jeanmonod, K., Germano, P., Koechli, C., Pataky, Z., … & Perriard, Y. (2023). A Polyester-Nylon Blend Plantar Pressure Sensing Insole for Person with Diabetes. IEEE Sensors Letters.
3. Hemler, S. L., Ntella, S. L., Jeanmonod, K., Köchli, C., Tiwari, B., Civet, Y., … & Pataky, Z. (2023). Intelligent plantar pressure offloading for the prevention of diabetic foot ulcers and amputations. Frontiers in Endocrinology, 14, 1166513.
. - Project # 7 – Experiment data processing of diabetic ulcer management system with the intelligent offloading footwear
- Andres Osorio Salazar
Every 20 seconds, someone with diabetes is subjected to amputation somewhere in the world. The primary risk factor for these amputations is the development of ulcerations in the skin, after abnormally high plantar pressures (PP) and/or shear stress [1], coupled with neuropathy, the lack of sensation in the feet. The plantar pressure distribution changes throughout the day according to various uncontrollable factors [2], such as activity, walking surface type, posture adjustments, etc. Therefore, an intelligent offloading system that can autonomously and continuously redistribute local PPs is highly desirable and could potentially reduce the incidence of amputations in people with diabetes.
In our laboratory, we have successfully developed an intelligent offloading shoe, which uses an in-house developed piezoresistive sensing insole to provide PP feedback to the controller, allowing the system to determine the appropriate offloading locations in real time [2]. The detected high-pressure areas are then offloaded using miniature magnetorheological fluid (MRF) modules that can change their stiffness according to an electromagnetic field [3].
The prototype has been used in initial testing with both healthy and diabetic participants, demonstrating promising results in redistributing plantar pressure effectively. These tests utilized a variety of measurement systems, including motion capture, a gait analysis carpet (GaitRite), plantar pressure sensors, and IMUs (inertial measurement units) embedded in the shoe. The data collected has provided information of the biomechanics of gait and the real-time response of the offloading system.
This project aims to quantitatively demonstrate the effectiveness of our prototype in managing plantar pressures. Specifically, we will analyze how the offloading functionality impacts state-of-the-art indicators for diabetic ulcer risk over time. By comparing the indicator’s classification before and after activating the offloading mechanism, as well as its variation in time, we will provide compelling evidence of the system’s ability to reduce ulceration risk.
Additionally, to better communicate the working principles, capabilities and results of this device, we aim to create engaging visual content, including animations, videos, and interactive plots. These materials will highlight the scientific and engineering foundation of the prototype, while also making the concept accessible to a broader audience, including healthcare professionals, researchers, and potential collaborators.
The following tasks will be done during the project:
– Evaluation of state of the art of indicators for risk of diabetic ulcer based on plantar pressure and gait parameters, including peak pressures, load per foot region, percentage medial impulse, Fourier power ratio, pressure time integral, supervised learning classification algorithms, etc.
– Implementation of indicators for risk of diabetic ulcer on previously or newly acquired data.
– Visualization of test data, including pressure redistribution plots, gait analysis metrics, and sensor outputs.
– Development of animations and videos explaining the shoe’s working principles, with a focus on clarity and impact
– Writing of a scientific project report and presentation
The following skills would be desirable for an applicant:
– Skills in data analysis, visualization and storage (e.g., MATLAB, Python, and Microsoft SQL server)
– Creativity in scientific communication, including designing animations and videos
– Teamwork and communication skills for effective collaboration and clear explanation of technical concepts
– Willingness to learn tools for visual representation (e.g., Blender, After Effects, or similar animation software)
With your participation in this project, you would not only advance the technology available for patients suffering from diabetes, but you would also benefit from the experience and knowledge of our team, and will gain skills in:
– Scientific Visualization: Creating high-impact animations and videos to explain complex concepts
– Data Representation: Learning advanced methods for processing and presenting experimental data
– Interdisciplinary Collaboration: Working alongside experts in biomechanics, engineering, and medical device development
– Scientific Communication: Enhancing your ability to translate research into accessible content for diverse audiences
References:
1. Lekkala, J. (2014). Plantar shear stress measurements—A review. Clinical Biomechanics, 29(5), 475-483.
2. Tiwari, B., Ntella, S. L., Jeanmonod, K., Germano, P., Koechli, C., Pataky, Z., … & Perriard, Y. (2023). A Polyester-Nylon Blend Plantar Pressure Sensing Insole for Person with Diabetes. IEEE Sensors Letters.
3. Hemler, S. L., Ntella, S. L., Jeanmonod, K., Köchli, C., Tiwari, B., Civet, Y., … & Pataky, Z. (2023). Intelligent plantar pressure offloading for the prevention of diabetic foot ulcers and amputations. Frontiers in Endocrinology, 14, 1166513.
. - Project # 8 – Transparent Neural Network for physics
- Marc Favier
This project aims at designing Deep learning architectures able to model friction behaviour. Based on existing data involving displacements between two surface we estimate the force created by the friction happening in between them. We will orient our work to increase explainaibility of our AI model. Deep Neural Networks have the flexibility to predict physical behaviours but lack of transparency.
Approach:
1. Data exploration/Collection:
– Depending on the current advancement on needs of the chosen model, additional data could be collected from existing measure bench
– Understanding of the pertinent values and their interaction, representation of their evolution during training and their physical meaning will be required
2. Model selection and training:
– Choose some pertinent models involving layers such as recurrent layer GRU, LSTM and attention
– implement a training pipeline and assess the performance of trained model
3. Performance evaluation:
– Design a challenging Nonlinear friction system on which our model can be evaluated
– Evaluate the model performance on the evaluation bench
4. Model understanding
– Run some statistics on correlation between inputs
– Define an approach (inspiration can be taken on gradient highlights from image processing Neural Networks, or model reduction) to explore results generated by the machine learning
Expected Outcomes:
– Design and development of a physical model of friction through AI
– Understanding of the used AI model
– Demonstration on a physical test bench of the prediction accuracy
Preferred Qualifications:
– The student should have a passion to Data Analysis and Artificial Intelligence
– The student should be interested in experimental lab work. Prior experience tinkering with hardware is preferable but not necessary. General curiosity and openness to try and learn new things will be appreciated
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